Prediction of Gross Calorific Value of Washed Turkish Lignite Coals with Support Vector Regression

Abstract

In this study, the gross calorific value (GCV) of washed Turkish lignite coals was predicted by using dry-basis proximate analysis data of coal samples with machine learning methods. The data set was generated by using ash (A), volatile matter (VM), sulfur (S) and GCV variables obtained from the analysis results. The GCV prediction models were developed by applying Support Vector Regression (SVR) and three different Artificial Neural Networks (ANNs), namely Multi-Layer Perceptron (MLP), General Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFN), separately to the data set. The generalization capability of the developed models was ensured by using 10-fold cross-validation, and the prediction accuracy of the models was calculated by using performance metrics Multiple Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). For GCV prediction, the results reveal that the SVR-based model performed slightly better than the ANN-based models and among the ANN-based models, the RBFN-based model performed better than MLP- and GRNN-based models.

Keywords

• Gross calorific value,support vector regression,multi-layer perceptron,general regression neural network,radial basis function neural network

Yıkanmış Türk Linyit Kömürlerinin Üst Isıl Değerinin Destek Vektör Regresyonu ile Tahmini

Abstract

Bu çalışmada yıkanmış Türk linyit kömürlerinin üst ısıl değeri (GCV), makine öğrenmesi yöntemleri ile kömür numunelerinin kuru baz kısa analiz sonuçları kullanılarak tahmin edilmiştir. Laboratuvar kömür analiz sonuçlarından elde edilen kül (A), uçucu madde (VM), kükürt (S) ve GCV değişkenleri kullanılarak veri kümesi oluşturulmuştur. Veri kümesine, Destek Vektör Regresyonu (SVR) ile Çok Katmanlı Algılayıcı (MLP), Genel Regresyon Sinir Ağı (GRNN) ve Radyal Temelli Fonksiyon Sinir Ağı (RBFN) olmak üzere üç farklı Yapay Sinir Ağı (ANN) uygulanarak GCV tahmin modelleri geliştirilmiştir. Geliştirilen modellerin performans genelleştirme kabiliyeti 10-katlı çapraz-doğrulama kullanılarak sağlanmış ve modellerin tahmin doğruluğu, performans ölçütleri Çoklu Korelasyon Katsayısı (R), Kök Ortalama Kare Hatası (RMSE), Ortalama Mutlak Hata (MAE) ve Ortalama Mutlak Yüzde Hata (MAPE) kullanılarak hesaplanmıştır. Sonuçlar, GCV tahmini için, tüm modeller arasında SVR tabanlı modelin ANN tabanlı modellere göre biraz daha iyi, ANN tabanlı modeller arasında ise RBFN tabanlı modelin MLP ve GRNN tabanlı modellere göre daha iyi performans gösterdiğini ortaya koymuştur.

Keywords

• Üst ısıl değer,destek vektör regresyonu,çok katmanlı algılayıcı,genel regresyon sinir ağı,radyal temelli fonksiyon sinir ağı

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